title: Validation of pediatric diabetes case identification approaches for diagnosed cases by using information in the electronic health records of a large integrated managed health care organization.
abstract: We explored the utility of different algorithms for diabetes case identification by using electronic health records. Inpatient and outpatient diagnosis codes, as well as data on laboratory results and dispensing of antidiabetic medications were extracted from electronic health records of Kaiser Permanente Southern California members who were less than 20 years of age in 2009. Diabetes cases were ascertained by using the SEARCH for Diabetes in Youth Study protocol and comprised the "gold standard." Sensitivity, specificity, positive and negative predictive values, accuracy, and the area under the receiver operating characteristic curve (AUC) were compared in 1,000 bootstrapped samples. Based on data from 792,992 youth, of whom 1,568 had diabetes (77.2%, type 1 diabetes; 22.2%, type 2 diabetes; 0.6%, other), case identification accuracy was highest in 75% of bootstrapped samples for those who had 1 or more outpatient diabetes diagnoses or 1 or more insulin prescriptions (sensitivity, 95.9%; positive predictive value, 95.5%; AUC, 97.9%) and in 25% of samples for those who had 2 or more outpatient diabetes diagnoses and 1 or more antidiabetic medications (sensitivity, 92.4%; positive predictive value, 98.4%; AUC, 96.2%). Having 1 or more outpatient type 1 diabetes diagnoses (International Classification of Diseases, Ninth Revision, Clinical Modification, code 250.x1 or 250.x3) had the highest accuracy (94.4%) and AUC (94.1%) for type 1 diabetes; the absence of type 1 diabetes diagnosis had the highest accuracy (93.8%) and AUC (93.6%) for identifying type 2 diabetes. Information in the electronic health records from managed health care organizations provides an efficient and cost-effective source of data for childhood diabetes surveillance.
authors.forename: Jean M
authors.initials: JM
authors.lastname: Lawrence
authors.forename: Mary Helen
authors.initials: MH
authors.lastname: Black
authors.forename: Jian L
authors.initials: JL
authors.lastname: Zhang
authors.forename: Jeff M
authors.initials: JM
authors.lastname: Slezak
authors.forename: Harpreet S
authors.initials: HS
authors.lastname: Takhar
authors.forename: Corinna
authors.initials: C
authors.lastname: Koebnick
authors.forename: Elizabeth J
authors.initials: EJ
authors.lastname: Mayer-Davis
authors.forename: Victor W
authors.initials: VW
authors.lastname: Zhong
authors.forename: Dana
authors.initials: D
authors.lastname: Dabelea
authors.forename: Richard F
authors.initials: RF
authors.lastname: Hamman
authors.forename: Kristi
authors.initials: K
authors.lastname: Reynolds
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001786:Blood Glucose; D002648:Child; D002675:Child, Preschool; D003922:Diabetes Mellitus, Type 1 / Q000175:diagnosis* / Q000453:epidemiology; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis* / Q000453:epidemiology; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D006306:Health Surveys; D006801:Humans; D007004:Hypoglycemic Agents / Q000008:administration & dosage*; D015994:Incidence; D007223:Infant; D038801:International Classification of Diseases; D008297:Male; D008329:Managed Care Programs / Q000706:statistics & numerical data*; D015995:Prevalence; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012959:Socioeconomic Factors
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents
keywords: adolescent; child; diabetes mellitus; electronic health records; sensitivity; specificity; type 1 diabetes mellitus; type 2 diabetes mellitus